Cameras have been developed which will automatically take photographs when particular conditions are satisfied, such as when a change in light level is detected or at particular time intervals. An example of such a camera is SenseCam, developed by Microsoft Research. There are many applications for SenseCam including use as a memory recall aid by enabling a user to browse through images of events experienced by a wearer of the device and use to create autobiographical media for sharing with friends.
In order that viewing the images captured automatically by such a camera is informative and to most effectively use the available memory and battery power of the camera, the captured images should be representative of events experienced by a wearer and should avoid undue repetition (i.e. the camera should avoid capturing multiple identical or very similar images). Existing devices enable capture conditions to be set based on inputs from accelerometers, passive infrared sensors or light sensors.
Image processing may be used to delete or hide images which are identical or very similar. However, such techniques are processor and power intensive and this therefore limits the applicability of such techniques to battery powered devices. Furthermore, whilst such techniques may be applied to adjacent images in a sequence of images, comparing large numbers of images may become computationally infeasible for a device with modest processing capabilities. Additionally, it is difficult to assess the similarity of images using existing image processing techniques where lighting changes or objects move and therefore such techniques, even where implemented, may not be particularly effective.
The embodiments described below are not limited to implementations which solve any or all of the disadvantages of known image capture devices.